An Unsupervised Machine Learning Approach to Evaluating the Association of Symptom Clusters With Adverse Outcomes Among Older Adults With Advanced Cancer: A Secondary Analysis of a Randomized Clinical Trial.

Importance: Older adults with advanced cancer who have high pretreatment symptom severity often experience adverse events during cancer treatments. Unsupervised machine learning may help stratify patients into different risk groups.

Objective: To evaluate whether clusters identified from baseline patient-reported symptom severity were associated with adverse outcomes.

Effects of a Geriatric Assessment Intervention on Patient-Reported Symptomatic Toxicity in Older Adults With Advanced Cancer.

Purpose: Providing a geriatric assessment (GA) summary with management recommendations to oncologists reduces clinician-rated toxicity in older patients with advanced cancer receiving treatment. This secondary analysis of a national cluster randomized clinical trial (ClinicalTrials.gov identifier: NCT02054741) aims to assess the effects of a GA intervention on symptomatic toxicity measured by Patient-Reported Outcomes Common Terminology Criteria for Adverse Events (PRO-CTCAE).